vsales <- read.csv("C:/Users/kaliy/OneDrive/Desktop/vsales.csv")
##vsales
Changing year from a string to a date
library(tidyverse)
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## ✔ purrr 1.0.1
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Year <- as.Date(as.character(vsales$Year), format = "%Y")
Year <- year(Year)
view(Year)
newvsales <- vsales%>%
select(Rank, Name, Year, Genre, Platform,NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales, Platform, Publisher )%>%
mutate(Year= Year)
library(ggplot2)
library("patchwork")
NAS <- newvsales%>%
select(Genre, NA_Sales)%>%
mutate(Genre = fct_lump(Genre,9))%>%
ggplot(aes(Genre, NA_Sales))+
geom_bar(stat = "identity") +
coord_flip()
JP <- newvsales%>%
select(Genre, JP_Sales)%>%
mutate(Genre = fct_lump(Genre,9))%>%
ggplot(aes(Genre, JP_Sales))+
geom_bar(stat = "identity")+
coord_flip()
EU <- newvsales%>%
select(Genre, EU_Sales)%>%
mutate(Genre = fct_lump(Genre,9))%>%
ggplot(aes(Genre, EU_Sales))+
geom_bar(stat = "identity")+
coord_flip()
Other<- newvsales%>%
select(Genre, Other_Sales)%>%
mutate(Genre = fct_lump(Genre,9))%>%
ggplot(aes(Genre, Other_Sales))+
geom_bar(stat = "identity")+
coord_flip()
NAS + EU+ JP + Other
Change of sales over time
library(patchwork)
c <- newvsales%>%
group_by(Year)%>%
summarise(sumNA = sum(NA_Sales))
p <- newvsales%>%
group_by(Year)%>%
summarise(sumEU = sum(EU_Sales))
f <- newvsales%>%
group_by(Year)%>%
summarise(sumJP = sum(JP_Sales))
a <- left_join(c, p , by ="Year")
g <- left_join(a ,f, by= "Year")
ggplot( g, aes(x =Year))+
geom_point(aes(y = sumNA, color = "NA"))+
geom_point(aes(y = sumEU, color = "EU")) +
geom_point(aes(y = sumJP, color = "JP"))+
coord_flip()
newvsales%>%
group_by(Publisher)%>%
summarise(pubna= sum(NA_Sales))%>%
arrange(desc(pubna))%>%
top_n(7, pubna)%>%
ggplot()+
aes(Publisher, pubna)+
geom_bar(stat = "identity")+
coord_flip()
allNA <- sum(newvsales$NA_Sales)
newvsales%>%
group_by(Publisher)%>%
summarise(pubna= sum(NA_Sales))%>%
top_n(10, pubna)%>%
mutate(per=( (pubna/allNA) *100))%>%
arrange(desc(per))%>%
ggplot( aes(x = "", y =per , fill = Publisher)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y" , start = 0)+
geom_text(aes(x=1, y = cumsum(per)-per/2, label=round(per))) +
labs(x =NULL, y = NULL, fill = NULL)
library("plotly")
##
## Attaching package: 'plotly'
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## last_plot
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## filter
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## layout
library(dplyr)
library("gapminder")
plot1 <- ggplot(data = newvsales, aes(x = Platform, y = NA_Sales)) +
geom_point()+
coord_flip()
ggplotly(plot1)